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[CourseClub.NET] Coursera - Applied Machine Learning in Python
magnet:?xt=urn:btih:2aebbd9a938b03ea4de16737994cb85b9fbdfd68&dn=[CourseClub.NET] Coursera - Applied Machine Learning in Python
磁力链接详情
Hash值:
2aebbd9a938b03ea4de16737994cb85b9fbdfd68
点击数:
133
文件大小:
880.52 MB
文件数量:
35
创建日期:
2020-6-25 08:14
最后访问:
2024-8-8 13:33
访问标签:
CourseClub
NET
Coursera
-
Applied
Machine
Learning
in
Python
文件列表详情
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/001. Introduction.mp4 31.05 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/002. Key Concepts in Machine Learning.mp4 44.56 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/003. Python Tools for Machine Learning.mp4 12.86 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/004. An Example Machine Learning Problem.mp4 31.73 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/005. Examining the Data.mp4 32.24 MB
001.Module 1 Fundamentals of Machine Learning - Intro to SciKit Learn/006. K-Nearest Neighbors Classification.mp4 36.25 MB
002.Module 2 Supervised Machine Learning/007. Introduction to Supervised Machine Learning.mp4 37.88 MB
002.Module 2 Supervised Machine Learning/008. Overfitting and Underfitting.mp4 19.51 MB
002.Module 2 Supervised Machine Learning/009. Supervised Learning Datasets.mp4 11.22 MB
002.Module 2 Supervised Machine Learning/010. K-Nearest Neighbors Classification and Regression.mp4 22.53 MB
002.Module 2 Supervised Machine Learning/011. Linear Regression Least-Squares.mp4 30.08 MB
002.Module 2 Supervised Machine Learning/012. Linear Regression Ridge, Lasso, and Polynomial Regression.mp4 39.93 MB
002.Module 2 Supervised Machine Learning/013. Logistic Regression.mp4 20.3 MB
002.Module 2 Supervised Machine Learning/014. Linear Classifiers Support Vector Machines.mp4 22.69 MB
002.Module 2 Supervised Machine Learning/015. Multi-Class Classification.mp4 15.41 MB
002.Module 2 Supervised Machine Learning/016. Kernelized Support Vector Machines.mp4 39.14 MB
002.Module 2 Supervised Machine Learning/017. Cross-Validation.mp4 20 MB
002.Module 2 Supervised Machine Learning/018. Decision Trees.mp4 37.83 MB
003.Module 3 Evaluation/019. Model Evaluation & Selection.mp4 46.1 MB
003.Module 3 Evaluation/020. Confusion Matrices & Basic Evaluation Metrics.mp4 20.75 MB
003.Module 3 Evaluation/021. Classifier Decision Functions.mp4 12.65 MB
003.Module 3 Evaluation/022. Precision-recall and ROC curves.mp4 9.23 MB
003.Module 3 Evaluation/023. Multi-Class Evaluation.mp4 19.77 MB
003.Module 3 Evaluation/024. Regression Evaluation.mp4 17.01 MB
003.Module 3 Evaluation/025. Model Selection Optimizing Classifiers for Different Evaluation Metrics.mp4 34.5 MB
004.Module 4 Supervised Machine Learning - Part 2/026. Naive Bayes Classifiers.mp4 21.38 MB
004.Module 4 Supervised Machine Learning - Part 2/027. Random Forests.mp4 26.45 MB
004.Module 4 Supervised Machine Learning - Part 2/028. Gradient Boosted Decision Trees.mp4 11.81 MB
004.Module 4 Supervised Machine Learning - Part 2/029. Neural Networks.mp4 41.51 MB
004.Module 4 Supervised Machine Learning - Part 2/030. Deep Learning (Optional).mp4 17.46 MB
004.Module 4 Supervised Machine Learning - Part 2/031. Data Leakage.mp4 32.89 MB
005.Optional Unsupervised Machine Learning/032. Introduction.mp4 10.67 MB
005.Optional Unsupervised Machine Learning/033. Dimensionality Reduction and Manifold Learning.mp4 16.09 MB
005.Optional Unsupervised Machine Learning/034. Clustering.mp4 27.18 MB
006.Conclusion/035. Conclusion.mp4 9.89 MB
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